# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MLap/bloom1.7-lora-sentiment-analysis-classification", dtype="auto")Quick Links
Model Description
- Developed by: Aman Prakash
- Model type: Decoder only LLM
- Language(s) (NLP): All suported by Bloom-1.7B, but primarily finetuned on English Dataset.
- License: MIT
- Finetuned from model: Bloom-1.7B
Uses
For Sentiment Analysis Classification with 3 class - 0 - negative, 1 - neutral, 2 - positive.
Downstream Use
Sentiment Analysis Classification
Training Details
FineTuning Data
Sp1786/multiclass-sentiment-analysis-dataset
[More Information Needed]
Training Procedure
LoRA with rank 8.
Training Hyperparameters
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=4,
eval_strategy="steps",
eval_steps=200,
logging_steps=100,
num_train_epochs=2,
learning_rate=2e-4,
fp16=True,
save_steps=500,
save_total_limit=2,
Model tree for MLap/bloom1.7-lora-sentiment-analysis-classification
Base model
bigscience/bloom
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MLap/bloom1.7-lora-sentiment-analysis-classification")